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The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.

Hysteresis in organic field-effect transistors is attributed to the well-known bias stress effects. This is a phenomenon in which the measured drain-source current varies when sweeping the gate voltage from on to off or from off to on. Hysteresis is caused by various factors, and one of the most common is charge trapping. A charge trap is a defect that occurs in an interface state or part of a semiconductor, and it refers to an electronic state that appears distributed in the semiconductor’s energy band gap. Extensive research has been conducted recently on obtaining a better understanding of charge traps for hysteresis. However, it is still difficult to accurately measure or characterize them, and their effects on the hysteresis of organic transistors remain largely unknown. In this study, we conduct a literature survey on the hysteresis caused by charge traps from various perspectives. We first analyze the driving principle of organic transistors and introduce various types of hysteresis. Subsequently, we analyze charge traps and determine their influence on hysteresis. In particular, we analyze various estimation models for the traps and the dynamics of the hysteresis generated through these traps. Lastly, we conclude this study by explaining the causal inference approach, which is a machine learning technique typically used for current data analysis, and its implementation for the quantitative analysis of the causal relationship between the hysteresis and the traps.

The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including Shapley Additive explanations feature selection, to improve model performance, robustness, and transparency. The method systematically employs different classifiers and proposes a new hybrid method called Hybrid Bagging-Boosting and Boosting on Residuals. Then, performance is taken in four steps: the multistep evaluation of hybrid ensemble learning methods for binary classification and fine-tuning of performance; feature selection using Shapley Additive explanations values retraining the hybrid model for better performance and reducing overfitting; the generalization of the proposed model for multiclass classification; and the evaluation using standard information metrics such as accuracy, precision, recall, and F1-score. Key results indicate that the proposed methods outperform state-of-the-art algorithms, achieving a peak accuracy of 98.47% and an F1 score of 96.19%. These improvements stem from advanced feature selection and resampling techniques, enhancing model accuracy and balancing precision and recall. Integrating Shapley Additive explanations-based feature selection with hybrid ensemble methods significantly boosts the predictive and explanatory power of Intrusion Detection and Prevention Systems, addressing common pitfalls in traditional cybersecurity models. This study paves the way for further research on statistical innovations to enhance Intrusion Detection and Prevention Systems performance.

Pathogens utilize multiple types of effectors to modulate plant immunity. Although many apoplastic and cytoplasmic effectors have been reported, nuclear effectors have not been well characterized in fungal pathogens. Here, we characterize two nuclear effectors of the rice blast pathogen Magnaporthe oryzae . Both nuclear effectors are secreted via the biotrophic interfacial complex, translocated into the nuclei of initially penetrated and surrounding cells, and reprogram the expression of immunity-associated genes by binding on effector binding elements in rice. Their expression in transgenic rice causes ambivalent immunity: increased susceptibility to M . oryzae and Xanthomonas oryzae pv. oryzae , hemibiotrophic pathogens, but enhanced resistance to Cochliobolus miyabeanus , a necrotrophic pathogen. Our findings help remedy a significant knowledge deficiency in the mechanism of M . oryzae –rice interactions and underscore how effector-mediated manipulation of plant immunity by one pathogen may also affect the disease severity by other pathogens. Plant pathogens secrete various effectors to manipulate host immunity. Here, Kim et al. describe two Magnaporthe oryzae effectors that translocate into the nuclei of infected rice cells and reprogram expression of immunity-associated genes, increasing susceptibility to hemibiotrophic pathogens.

We assessed the field-scale stabilization efficiency of lead (Pb) and copper (Cu) in contaminated soils using powdered (WOS-P) and granular (WOS-G) waste oyster shell (WOS) amendments. A 9-month monitoring was conducted using geochemical leaching tests Toxicity Characteristic Leaching Procedure (TCLP), Mehlich-3, and Diethylenetriaminepentaacetic acid (DTPA) besides sequential extraction and geophysical methods. WOS-treated soils exhibited significant reductions in leaching ratios, with WOS-P showing greater efficacy. Pb and Cu leaching ratios decreased by up to 1.21 and 2.00% points, respectively. Fractional analysis confirmed the redistribution of Pb into carbonate-bound forms, with the F2 fraction increasing from 6.46 to 12.89%, and Cu into Fe–Mn oxide-bound forms (F3), which increased from 11.29 to 15.88%. Electrical resistivity (ER) and induced polarization (IP) surveys visualized the spatial evolution of stabilization. In WOS-P plots, lower ER and elevated IP responses were initially observed, consistent with those of increased ionic strength. Over time, signal attenuation suggested precipitation and geochemical fixation. WOS-G plots showed delayed IP enhancement, reflecting slower dissolution and ion release. Our results suggest that WOS can serve as an effective and sustainable stabilizer, and that combined ER and IP with geochemical monitoring offers valuable complementary insights. Future work should expand the spatial and temporal scope to validate these findings and advance integrated interpretation frameworks for more robust field applicability and quantitative stabilization assessment.

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Background Cytochrome P450 proteins (CYPs) play diverse and pivotal roles in fungal metabolism and adaptation to specific ecological niches. Fungal genomes encode extremely variable “CYPomes” ranging from one to more than 300 CYPs. Despite the rapid growth of sequenced fungal and oomycete genomes and the resulting influx of predicted CYPs, the vast majority of CYPs remain functionally uncharacterized. To facilitate the curation and functional and evolutionary studies of CYPs, we previously developed Fungal Cytochrome P450 Database (FCPD), which included CYPs from 70 fungal and oomycete species. Here we present a new version of FCPD (1.2) with more data and an improved classification scheme. Results The new database contains 22,940 CYPs from 213 species divided into 2,579 clusters and 115 clans. By optimizing the clustering pipeline, we were able to uncover 36 novel clans and to assign 153 orphan CYP families to specific clans. To augment their functional annotation, CYP clusters were mapped to David Nelson’s P450 databases, which archive a total of 12,500 manually curated CYPs. Additionally, over 150 clusters were functionally classified based on sequence similarity to experimentally characterized CYPs. Comparative analysis of fungal and oomycete CYPomes revealed cases of both extreme expansion and contraction. The most dramatic expansions in fungi were observed in clans CYP58 and CYP68 (Pezizomycotina), clans CYP5150 and CYP63 (Agaricomycotina), and family CYP509 (Mucoromycotina). Although much of the extraordinary diversity of the pan-fungal CYPome can be attributed to gene duplication and adaptive divergence, our analysis also suggests a few potential horizontal gene transfer events. Updated families and clans can be accessed through the new version of the FCPD database. Conclusions FCPD version 1.2 provides a systematic and searchable catalogue of 9,550 fungal CYP sequences (292 families) encoded by 108 fungal species and 147 CYP sequences (9 families) encoded by five oomycete species. In comparison to the first version, it offers a more comprehensive clan classification, is fully compatible with Nelson’s P450 databases, and has expanded functional categorization. These features will facilitate functional annotation and classification of CYPs encoded by newly sequenced fungal and oomycete genomes. Additionally, the classification system will aid in studying the roles of CYPs in the evolution of fungal adaptation to specific ecological niches.

Personalized healthcare recommender systems are increasingly being deployed in edge AI environments through wearable devices. In such environments, cloud servers leverage high-performance GPUs to train base models, which are then optimized for data reduction deployment on edge devices, enabling the delivery of personalized services. However, the base model may experience a gradual decline in accuracy over time, a phenomenon known as model drift. Recommender systems that do not keep up with changes in user preferences risk generating predictions based on outdated behavior, which can negatively impact the user experience. Therefore, it is essential to adopt retraining approaches that incorporate both past training data and new data from wearable devices. To address the drift problem, we propose a dynamic data management strategy, integrated into an automated training pipeline based on machine learning operations (MLOps). This approach enables adaptive model updates in response to continuously evolving IoT data. To preserve base model performance, our strategy leverages data reduction and feature selection algorithms. By dynamically managing data with these techniques, we effectively mitigate data drift and enhance resource efficiency during model retraining. We validated our approach through experiments on personalized fitness recommendations using FitRec wearable data from 1104 users, achieving improved computational efficiency during retraining while preserving model accuracy. Consequently, our dynamic data management method ensures faster training and the sustained performance of data reduction base models essential for edge AI applications. Moreover, this approach presents a compelling solution for continuously refining personalized recommendation services in alignment with evolving user preferences.

Underwater environmental exploration using sensor nodes has emerged as a critical endeavor fraught with challenges such as localization errors, energy, and costs attributed to the dynamic nature of underwater environments. This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. By addressing existing localization challenges, the algorithm minimizes localization errors, energy consumption and Time costs while significantly enhancing localization accuracy to 99.98%. Furthermore, the study employs the KNN-based cost-efficient method to predict nodes’ orientation in dynamic water conditions, thereby facilitating the mapping of the shortest distance between sensor nodes during the underwater context acquisition process. The effectiveness of the proposed KNN-based cost-efficient method is evaluated through real-time experiments conducted in a water tank setup and simulations using the Ns-3.37 version. Results demonstrate notable improvements in localization accuracy by optimizing the localization error rate from 4.59m to m, Reducing localization energy consumption rate 0.0045J in addition for the first time we have also computed the localization Time cost rate which is 0.06762s. we assumed that in real-time and in NS-3 simulations on the Aqua-sim model indicate communication speed at 1500m/s. This research presents an innovative and practical approach to resolving challenges associated with underwater context acquisition through sensor nodes, it offers a comprehensive understanding and emphasizes the real-time implementation of the KNN-based cost-efficient approach.